Prediction and Sequential Decision Making

1. M. Feder, N. Merhav, and M. Gutman, ``Universal prediction of individual sequences,'' IEEE Trans. Inform. Theory, vol. 38, no. 4, pp. 1258-1270, July 1992 (received the 1993 paper award of the Information Theory Society).
2. N. Merhav, M. Feder, and M. Gutman, ``Some properties of sequential predictors for binary Markov sources,'' IEEE Trans. Inform. Theory, vol. 39, no. 3, pp. 887-892, May 1993.
3. N. Merhav and M. Feder, ``Universal schemes for sequential decision from individual data sequences,'' IEEE Trans. Inform. Theory vol. 39, no. 4, pp. 1280-1291, July 1993.
4. R. Meir and N. Merhav, ``On the stochastic complexity of learning realizable and unrealizable rules,'' Machine Learning vol. 19, no. 3, pp. 241-261, 1995.
5. N. Merhav and M. Feder, ``Universal prediction,'' (invited paper) IEEE Trans. Inform. Theory, vol. 44, no. 6, pp. 2124-2147, October 1998. (Commemorative issue for fifty years of Information Theory.) Also, in Information Theory: 50 Years of Discovery, pp. 80-103, Eds. S. Verdu and S. McLaughlin, IEEE Press, 1999.
6. A. Baruch and N. Merhav, ``Universal filtering and prediction of individual sequences corrupted by noise using the Lempel-Ziv algorithm,'' Proc. 2000 IEEE Int. Symp. on Information Theory (ISIT 2000), p. 99, Sorrento, Italy, June 2000.
7. T. Weissman and N. Merhav, ``Universal prediction of individual binary sequences in the presence of arbitrarily varying, memoryless, additive noise,'' Proc. 2000 IEEE Int. Symp. on Information Theory (ISIT 2000), p. 97, Sorrento, Italy, June 2000.
8. T. Weissman, N. Merhav, and A. Somekh-Baruch, ``Twofold universal prediction schemes for achieving the finite state predictability of a noisy individual binary sequence,'' IEEE Trans. Inform. Theory, vol 47, no. 5, pp. 1849-1866, July 2001.
9. T. Weissman and N. Merhav, ``Universal prediction of binary individual sequences in the presence of noise,'' IEEE Trans. Inform. Theory, vol. 47, no. 6, pp. 2151-2173, September 2001.
10. N. Merhav, E. Ordentlich, G. Seroussi, and M. J. Weinberger, ``On sequential strategies for loss functions with memory,'' IEEE Trans. Inform. Theory, vol. 48, no. 7, pp. 1947-1958, July 2002.
11. N. Merhav and T. Weissman, ``Scanning and prediction in multi-dimensional data arrays,'' IEEE Trans. Inform. Theory, vol. 49, no. 1, pp. 65-82, January 2003.
12. T. Weissman and N. Merhav, ``On competitive predictability and its relation to rate-distortion theory and to channel capacity theory,'' IEEE Trans. Inform. Theory, vol. 49, no. 12, pp. 3185-3194, December 2003.
13. T. Weissman and N. Merhav, ``Universal prediction of random binary sequences in a noisy environment,'' Annals of Applied Probability, vol. 14, no. 1, pp. 54-89. February 2004.
14. E. Ordentlich, T. Weissman, M. J. Weinberger, A. Somekh-Baruch, and N. Merhav, ``Discrete universal filtering through incremental parsing,'' Proc. DCC 2004, Snowbird, Utah, March 2004.
15. E. Sabbag and N. Merhav, ``Large deviations performance of predictors for Markov sources,'' Proc. ISIT 2004 , p. 11, Chicago, IL, June-July 2004.
16. J. Ziv and N. Merhav, ``On context-tree prediction of individual sequences,'' IEEE Trans. Inform. Theory, vol. 53, no. 5, pp. 1860-1866, May 2007.
17. T. Weissman, E. Ordentlich, M. J. Weinberger, A. Somekh-Baruch, and N. Merhav,'' ``Universal filtering via prediction,'' IEEE Trans. Inform. Theory, vol. 53, no. 4, pp. 1253-1264, April 2007.
18. A. Cohen, N. Merhav, and T. Weissman, ``Scanning and sequential decision making for multi-dimensional data: part I - the noiseless case,'' IEEE Trans. Inform. Theory, vol. 53, no. 9, pp. 3001-3020, September 2007.
19. A. Cohen, T. Weissman, and N. Merhav, ``Scanning and sequential decision making for multi-dimensional data: part II - the noisy case,'' IEEE Trans. Inform. Theory, vol. 54, no. 12, pp. 5609-5631, December 2008.